

'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


lowerCAmelCase__   		=      random.Random()


def 						_A	(       A__	,							A__=1.0	,							A__=None	,							A__=None ):






 """simple docstring"""



 if rng is None:
  __lowercase				    =					global_rng

 __lowercase				    =					[]
 for batch_idx in range(shape[0] ):
  values.append([] )
  for _ in range(shape[1] ):
   values[-1].append(rng.random() * scale )

 return values






@require_torch
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def __init__( self  :	Optional[Any]   ,lowercase__  :	List[str]   ,lowercase__  :	str=7   ,lowercase__  :	List[str]=4_0_0   ,lowercase__  :	Optional[Any]=2_0_0_0   ,lowercase__  :	int=1   ,lowercase__  :	Optional[Any]=0.0   ,lowercase__  :	Tuple=1_6_0_0_0   ,lowercase__  :	str=True   ,lowercase__  :	Optional[int]=8_0   ,lowercase__  :	List[str]=1_6   ,lowercase__  :	Optional[Any]=6_4   ,lowercase__  :	Dict="hann_window"   ,lowercase__  :	Tuple=8_0   ,lowercase__  :	Tuple=7_6_0_0   ,lowercase__  :	int=1e-1_0   ,lowercase__  :	Dict=True   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					min_seq_length
   __lowercase				    =					max_seq_length
   __lowercase				    =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase				    =					feature_size
   __lowercase				    =					padding_value
   __lowercase				    =					sampling_rate
   __lowercase				    =					do_normalize
   __lowercase				    =					num_mel_bins
   __lowercase				    =					hop_length
   __lowercase				    =					win_length
   __lowercase				    =					win_function
   __lowercase				    =					fmin
   __lowercase				    =					fmax
   __lowercase				    =					mel_floor
   __lowercase				    =					return_attention_mask






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   return {
       "feature_size": self.feature_size,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "do_normalize": self.do_normalize,
       "num_mel_bins": self.num_mel_bins,
       "hop_length": self.hop_length,
       "win_length": self.win_length,
       "win_function": self.win_function,
       "fmin": self.fmin,
       "fmax": self.fmax,
       "mel_floor": self.mel_floor,
       "return_attention_mask": self.return_attention_mask,
   }






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	Optional[int]=False   ,lowercase__  :	Any=False     ):
   def _flatten(lowercase__  :	List[str]     ):
    return list(itertools.chain(*lowercase__     )     )

   if equal_length:
    __lowercase				    =					floats_list((self.batch_size, self.max_seq_length)     )
   else:
    # make sure that inputs increase in size
    __lowercase				    =					[
        _flatten(floats_list((x, self.feature_size)     )     )
        for x in range(self.min_seq_length   ,self.max_seq_length   ,self.seq_length_diff     )
    ]

   if numpify:
    __lowercase				    =					[np.asarray(lowercase__     ) for x in speech_inputs]

   return speech_inputs






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	Dict=False   ,lowercase__  :	Optional[Any]=False     ):
   if equal_length:
    __lowercase				    =					[floats_list((self.max_seq_length, self.num_mel_bins)     ) for _ in range(self.batch_size     )]
   else:
    # make sure that inputs increase in size
    __lowercase				    =					[
        floats_list((x, self.num_mel_bins)     )
        for x in range(self.min_seq_length   ,self.max_seq_length   ,self.seq_length_diff     )
    ]

   if numpify:
    __lowercase				    =					[np.asarray(lowercase__     ) for x in speech_inputs]

   return speech_inputs






@require_torch
class      lowercase_      (lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     SpeechTaFeatureExtractor






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					SpeechTaFeatureExtractionTester(self     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	int     ):
   self.assertTrue(np.all(np.mean(lowercase__   ,axis=0     ) < 1e-3     )     )
   self.assertTrue(np.all(np.abs(np.var(lowercase__   ,axis=0     ) - 1     ) < 1e-3     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   # create three inputs of length 800, 1000, and 1200
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					[np.asarray(lowercase__     ) for speech_input in speech_inputs]

   # Test not batched input
   __lowercase				    =					feat_extract(speech_inputs[0]   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feat_extract(np_speech_inputs[0]   ,return_tensors='''np'''     ).input_values
   self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-3     )     )

   # Test batched
   __lowercase				    =					feat_extract(lowercase__   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feat_extract(lowercase__   ,return_tensors='''np'''     ).input_values
   for enc_seq_a, enc_seq_a in zip(lowercase__   ,lowercase__     ):
    self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-3     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]

   __lowercase				    =					['''longest''', '''max_length''', '''do_not_pad''']
   __lowercase				    =					[None, 1_6_0_0, None]
   for max_length, padding in zip(lowercase__   ,lowercase__     ):
    __lowercase				    =					feat_extract(lowercase__   ,padding=lowercase__   ,max_length=lowercase__   ,return_tensors='''np'''     )
    __lowercase				    =					processed.input_values

    self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
    self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6     )
    self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
    self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6     )
    self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					range(8_0_0   ,1_4_0_0   ,2_0_0     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in lengths]

   __lowercase				    =					['''longest''', '''max_length''', '''do_not_pad''']
   __lowercase				    =					[None, 1_6_0_0, None]

   for max_length, padding in zip(lowercase__   ,lowercase__     ):
    __lowercase				    =					feat_extract(lowercase__   ,max_length=lowercase__   ,padding=lowercase__     )
    __lowercase				    =					processed.input_values

    self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
    self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
    self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					feat_extract(
       lowercase__   ,truncation=lowercase__   ,max_length=1_0_0_0   ,padding='''max_length'''   ,return_tensors='''np'''     )
   __lowercase				    =					processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
   self._check_zero_mean_unit_variance(input_values[1]     )
   self._check_zero_mean_unit_variance(input_values[2]     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					feat_extract(
       lowercase__   ,truncation=lowercase__   ,max_length=1_0_0_0   ,padding='''longest'''   ,return_tensors='''np'''     )
   __lowercase				    =					processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
   self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
   self._check_zero_mean_unit_variance(input_values[2]     )

   # make sure that if max_length < longest -> then pad to max_length
   self.assertTrue(input_values.shape == (3, 1_0_0_0)     )

   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					feat_extract(
       lowercase__   ,truncation=lowercase__   ,max_length=2_0_0_0   ,padding='''longest'''   ,return_tensors='''np'''     )
   __lowercase				    =					processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
   self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
   self._check_zero_mean_unit_variance(input_values[2]     )

   # make sure that if max_length > longest -> then pad to longest
   self.assertTrue(input_values.shape == (3, 1_2_0_0)     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   __lowercase				    =					np.random.rand(1_0_0     ).astype(np.floataa     )
   __lowercase				    =					np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __lowercase				    =					feature_extractor.pad([{'''input_values''': inputs}]   ,return_tensors='''np'''     )
    self.assertTrue(np_processed.input_values.dtype == np.floataa     )
    __lowercase				    =					feature_extractor.pad([{'''input_values''': inputs}]   ,return_tensors='''pt'''     )
    self.assertTrue(pt_processed.input_values.dtype == torch.floataa     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
   # create three inputs of length 800, 1000, and 1200
   __lowercase				    =					[floats_list((1, x)     )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0     )]
   __lowercase				    =					[np.asarray(lowercase__     ) for speech_input in speech_inputs]

   # Test feature size
   __lowercase				    =					feature_extractor(audio_target=lowercase__   ,padding=lowercase__   ,return_tensors='''np'''     ).input_values
   self.assertTrue(input_values.ndim == 3     )
   self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins     )

   # Test not batched input
   __lowercase				    =					feature_extractor(speech_inputs[0]   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feature_extractor(np_speech_inputs[0]   ,return_tensors='''np'''     ).input_values
   self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-3     )     )

   # Test batched
   __lowercase				    =					feature_extractor(lowercase__   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feature_extractor(lowercase__   ,return_tensors='''np'''     ).input_values
   for enc_seq_a, enc_seq_a in zip(lowercase__   ,lowercase__     ):
    self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-3     )     )

   # Test 2-D numpy arrays are batched.
   __lowercase				    =					[floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase				    =					np.asarray(lowercase__     )
   __lowercase				    =					feature_extractor(lowercase__   ,return_tensors='''np'''     ).input_values
   __lowercase				    =					feature_extractor(lowercase__   ,return_tensors='''np'''     ).input_values
   for enc_seq_a, enc_seq_a in zip(lowercase__   ,lowercase__     ):
    self.assertTrue(np.allclose(lowercase__   ,lowercase__   ,atol=1e-3     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.feat_extract_tester.prepare_inputs_for_target()
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_dict     )
   __lowercase				    =					feat_extract.model_input_names[0]

   __lowercase				    =					BatchFeature({input_name: speech_inputs}     )

   self.assertTrue(all(len(lowercase__     ) == len(lowercase__     ) for x, y in zip(lowercase__   ,processed_features[input_name]     )     )     )

   __lowercase				    =					self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase__     )
   __lowercase				    =					BatchFeature({input_name: speech_inputs}   ,tensor_type='''np'''     )

   __lowercase				    =					processed_features[input_name]

   if len(batch_features_input.shape     ) < 3:
    __lowercase				    =					batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.num_mel_bins)     )






  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase__     )
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_dict     )
   __lowercase				    =					feat_extract.model_input_names[0]

   __lowercase				    =					BatchFeature({input_name: speech_inputs}   ,tensor_type='''pt'''     )

   __lowercase				    =					processed_features[input_name]

   if len(batch_features_input.shape     ) < 3:
    __lowercase				    =					batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.num_mel_bins)     )






  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					self.feature_extraction_class(**self.feat_extract_dict     )
   __lowercase				    =					self.feat_extract_tester.prepare_inputs_for_target()
   __lowercase				    =					feat_extract.model_input_names[0]

   __lowercase				    =					BatchFeature({input_name: speech_inputs}     )

   __lowercase				    =					feat_extract.num_mel_bins  # hack!

   __lowercase				    =					feat_extract.pad(lowercase__   ,padding='''longest'''   ,return_tensors='''np'''     )[input_name]
   __lowercase				    =					feat_extract.pad(lowercase__   ,padding='''longest'''   ,return_tensors='''pt'''     )[input_name]

   self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_pt.numpy().astype(np.floataa     ).sum()     ) < 1e-2     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.feat_extract_dict
   __lowercase				    =					True
   __lowercase				    =					self.feature_extraction_class(**lowercase__     )
   __lowercase				    =					self.feat_extract_tester.prepare_inputs_for_target()
   __lowercase				    =					[len(lowercase__     ) for x in speech_inputs]
   __lowercase				    =					feat_extract.model_input_names[0]

   __lowercase				    =					BatchFeature({input_name: speech_inputs}     )

   __lowercase				    =					feat_extract.num_mel_bins  # hack!

   __lowercase				    =					feat_extract.pad(lowercase__   ,padding='''longest'''   ,return_tensors='''np'''     )
   self.assertIn('''attention_mask'''   ,lowercase__     )
   self.assertListEqual(list(processed.attention_mask.shape     )   ,list(processed[input_name].shape[:2]     )     )
   self.assertListEqual(processed.attention_mask.sum(-1     ).tolist()   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.feat_extract_dict
   __lowercase				    =					True
   __lowercase				    =					self.feature_extraction_class(**lowercase__     )
   __lowercase				    =					self.feat_extract_tester.prepare_inputs_for_target()
   __lowercase				    =					[len(lowercase__     ) for x in speech_inputs]
   __lowercase				    =					feat_extract.model_input_names[0]

   __lowercase				    =					BatchFeature({input_name: speech_inputs}     )
   __lowercase				    =					min(lowercase__     )

   __lowercase				    =					feat_extract.num_mel_bins  # hack!

   __lowercase				    =					feat_extract.pad(
       lowercase__   ,padding='''max_length'''   ,max_length=lowercase__   ,truncation=lowercase__   ,return_tensors='''np'''     )
   self.assertIn('''attention_mask'''   ,lowercase__     )
   self.assertListEqual(
       list(processed_pad.attention_mask.shape     )   ,[processed_pad[input_name].shape[0], max_length]     )
   self.assertListEqual(
       processed_pad.attention_mask[:, :max_length].sum(-1     ).tolist()   ,[max_length for x in speech_inputs]     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	str     ):
   from datasets import load_dataset

   __lowercase				    =					load_dataset('''hf-internal-testing/librispeech_asr_dummy'''   ,'''clean'''   ,split='''validation'''     )
   # automatic decoding with librispeech
   __lowercase				    =					ds.sort('''id'''     ).select(range(lowercase__     )     )[:num_samples]['''audio''']

   return [x["array"] for x in speech_samples]






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   # fmt: off
   __lowercase				    =					torch.tensor(
       [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
        3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
        2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
        4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
        7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
        4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]     )
   # fmt: on

   __lowercase				    =					self._load_datasamples(1     )
   __lowercase				    =					SpeechTaFeatureExtractor()
   __lowercase				    =					feature_extractor(lowercase__   ,return_tensors='''pt'''     ).input_values
   self.assertEquals(input_values.shape   ,(1, 9_3_6_8_0)     )
   self.assertTrue(torch.allclose(input_values[0, :3_0]   ,lowercase__   ,atol=1e-6     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   # fmt: off
   __lowercase				    =					torch.tensor(
       [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
        -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
        -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
        -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]     )
   # fmt: on

   __lowercase				    =					self._load_datasamples(1     )
   __lowercase				    =					SpeechTaFeatureExtractor()
   __lowercase				    =					feature_extractor(audio_target=lowercase__   ,return_tensors='''pt'''     ).input_values
   self.assertEquals(input_values.shape   ,(1, 3_6_6, 8_0)     )
   self.assertTrue(torch.allclose(input_values[0, 0, :3_0]   ,lowercase__   ,atol=1e-4     )     )



"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      ['''image_processor''', '''tokenizer''']
       UpperCamelCase_		:       Dict				=      '''BlipImageProcessor'''
       UpperCamelCase_		:       Optional[int]				=      '''AutoTokenizer'''






       def __init__(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_							,   UpperCAmelCase_      )

              # add QFormer tokenizer
              UpperCamelCase       			=		qformer_tokenizer






       def __call__(	self			:				Tuple							,   UpperCAmelCase_			:				ImageInput = None							,   UpperCAmelCase_			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Union[bool, str, PaddingStrategy] = False							,   UpperCAmelCase_			:				Union[bool, str, TruncationStrategy] = None							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Optional[Union[str, TensorType]] = None							,   **UpperCAmelCase_			:				str							,   )->	BatchFeature:







              """simple docstring"""
              if images is None and text is None:
                     raise ValueError("You have to specify at least images or text."      )

              UpperCamelCase       			=		BatchFeature()

              if text is not None:
                     UpperCamelCase       			=		self.tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     encoding.update(UpperCAmelCase_      )
                     UpperCamelCase       			=		self.qformer_tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     UpperCamelCase       			=		qformer_text_encoding.pop("input_ids"      )
                     UpperCamelCase       			=		qformer_text_encoding.pop("attention_mask"      )

              if images is not None:
                     UpperCamelCase       			=		self.image_processor(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_      )
                     encoding.update(UpperCAmelCase_      )

              return encoding






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				List[str]      )->	Dict:







              """simple docstring"""
              return self.tokenizer.batch_decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   *UpperCAmelCase_			:				Dict							,   **UpperCAmelCase_			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              return self.tokenizer.decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer.model_input_names
              UpperCamelCase       			=		self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Tuple							,   **UpperCAmelCase_			:				Tuple      )->	Union[str, Any]:







              """simple docstring"""
              if os.path.isfile(UpperCAmelCase_      ):
                     raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"      )
              os.makedirs(UpperCAmelCase_							,   exist_ok=UpperCAmelCase_      )
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_							,   "qformer_tokenizer"      )
              self.qformer_tokenizer.save_pretrained(UpperCAmelCase_      )
              return super().save_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Dict							,   UpperCAmelCase_			:				int							,   **UpperCAmelCase_			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		AutoTokenizer.from_pretrained(UpperCAmelCase_							,   subfolder="qformer_tokenizer"      )
              UpperCamelCase       			=		cls._get_arguments_from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
              args.append(UpperCAmelCase_      )
              return cls(*UpperCAmelCase_      )





